Weakly-Supervised Temporal Action Detection
aims to tackle temporal action localization in the weakly-supervised setting. Video label is the most common supervision in practice.
Most existing models follow the “localization by classification” procedure and aggregates frame class scores to produce video prediction. BaS-Net  [1]} introduces an auxiliary class for background and suppress activations from background frames.
Meanwhile, FAC-Net  [2]} takes the foreground-action consistency into consideration and models the bilateral relations.
Another method has noticed the contrastive learning. CoLA  [3]} forms hard-easy snippet contrastive pairs and conduct the feature refinement via their proposed contrastive loss. It is significantly differently from ours in that it relies on pseudo action labels for defining positive pairs and negative pairs, while we formula it as an instance discrimination task. Recently, point supervision becomes a new research hotspot. SF-Net [4]} mines pseudo action and background frames. Recently, LACP [5]} improves performance by dense pseudo-labels that is likely to contain complete action instances.
